Library of existing spoken dialog data for use in generating new natural language spoken dialog systems

ABSTRACT

A machine-readable medium may include a group of reusable components for building a spoken dialog system. The reusable components may include a group of previously collected audible utterances. A machine-implemented method to build a library of reusable components for use in building a natural language spoken dialog system may include storing a dataset in a database. The dataset may include a group of reusable components for building a spoken dialog system. The reusable components may further include a group of previously collected audible utterances. A second method may include storing at least one set of data. Each one of the at least one set of data may include ones of the reusable components associated with audible data collected during a different collection phase.

PRIORITY

The present application is a continuation of U.S. patent applicationSer. No. 11/029,319, filed Jan. 5, 2005, which is incorporated herein byreference in its entirety.

RELATED APPLICATIONS

The present invention is related to U.S. patent application Ser. No.11/029,317, filed Jan. 5, 2005, entitled “A SYSTEM AND METHOD FOR USINGA LIBRARY OF DATA TO INTERACTIVELY DESIGN NATURAL LANGUAGE SPOKEN DIALOGSYSTEMS,” U.S. patent application Ser. No. 11/029,798, filed Jan. 5,2005, now U.S. Pat. No. 8,185,399, entitled “A SYSTEM OF PROVIDING ANAUTOMATED DATA-COLLECTION IN SPOKEN DIALOG SYSTEMS,” and U.S. patentapplication Ser. No. 11/029,318, filed Jan. 5, 2005, entitled“BOOTSTRAPPING SPOKEN DIALOG SYSTEMS WITH DATA REUSE.” The contents ofthe above U.S. Patent Applications are herein incorporated by referencein their entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to speech processing and more specificallyto reusing existing spoken dialog data to generate a new naturallanguage spoken dialog system.

2. Introduction

Natural language spoken dialog systems receive spoken language as input,analyze the received spoken language input to derive meaning from theinput, and perform some action, which may include generating speech,based on the meaning derived from the input. Building natural languagespoken dialog systems requires large amounts of human intervention. Forexample, a number of recorded speech utterances may require manualtranscription and labeling for the system to reach a useful level ofperformance for operational service. In addition, the design of suchcomplex systems typically includes a human being, such as, a UserExperience (UE) expert to manually analyze and define system corefunctionalities, such as, a system's semantic scope (call-types andnamed entities) and a dialog manager strategy, which will drive thehuman-machine interaction. This approach to building natural languagespoken dialog systems is extensive and error prone because it involvesthe UE expert making non-trivial design decisions, the results of whichcan only be evaluated after the actual system deployment. Thus, acomplex system may require the UE expert to define the system's corefunctionalities via several design cycles that may include defining orredefining the core functionalities, deploying the system, and analyzingthe performance of the system. Moreover, scalability is compromised bytime, costs and the high level of UE know-how needed to reach aconsistent design. A new approach that reduces the amount of humanintervention required to build a natural language spoken dialog systemis desired.

SUMMARY OF THE INVENTION

In a first aspect of the invention, a machine-readable medium isprovided. The machine-readable medium may include a group of reusablecomponents for building a spoken dialog system. The reusable componentsmay include a group of previously collected audible utterances. In someimplementations consistent with the principles of the invention, thecollected audible utterances may be transcribed and semantically labeled(e.g., with associated call-types and named entities).

In a second aspect of the invention, a machine-implemented method tobuild a library of reusable components for use in building a naturallanguage spoken dialog system is provided. The method may includestoring a dataset in a database. The dataset may include a group ofreusable components for building a spoken dialog system. The reusablecomponents may further include a group of previously collected audibleutterances. In some implementations consistent with the principles ofthe invention, the collected audible utterances may be optionallytranscribed and semantically labeled (e.g., with associated call-typesand named entities).

In a third aspect of the invention, a method to build a library ofreusable components for use in building a natural language spoken dialogsystem is provided. The method may include storing at least one set ofdata, each one of the at least one set of data including ones of thereusable components associated with audible data collected during adifferent collection phase.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate an embodiment of the inventionand, together with the description, explain the invention. In thedrawings,

FIG. 1 is a functional block diagram of an exemplary natural languagespoken dialog system;

FIG. 2 illustrates an exemplary processing system that may be used toimplement one or more components of the exemplary natural languagespoken dialog system of FIG. 1;

FIG. 3 shows an exemplary architecture of a library consistent with theprinciples of the invention; and

FIGS. 4 and 5 are flowcharts that explain an exemplary process forbuilding a library of reusable components consistent with the principlesof the invention.

DETAILED DESCRIPTION OF THE INVENTION Natural Language Spoken DialogSystems

Various embodiments of the invention are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without parting from the spirit and scope of the invention.

FIG. 1 is a functional block diagram of an exemplary natural languagespoken dialog system 100. Natural language spoken dialog system 100 mayinclude an automatic speech recognition (ASR) module 102, a spokenlanguage understanding (SLU) module 104, a dialog management (DM) module106, a spoken language generation (SLG) module 108, and a text-to-speech(TTS) module 110.

ASR module 102 may analyze speech input and may provide a transcriptionof the speech input as output. SLU module 104 may receive thetranscribed input and may use a natural language understanding model toanalyze the group of words that are included in the transcribed input toderive a meaning from the input. DM module 106 may receive the meaningof the speech input as input and may determine an action, such as, forexample, providing a spoken response, based on the input. SLG module 108may generate a transcription of one or more words in response to theaction provided by DM 106. TTS module 110 may receive the transcriptionas input and may provide generated audible speech as output based on thetranscribed speech.

Thus, the modules of system 100 may recognize speech input, such asspeech utterances, may transcribe the speech input, may identify (orunderstand) the meaning of the transcribed speech, may determine anappropriate response to the speech input, may generate text of theappropriate response and from that text, generate audible “speech” fromsystem 100, which the user then hears. In this manner, the user cancarry on a natural language dialog with system 100. Those of ordinaryskill in the art will understand the programming languages and means forgenerating and training ASR module 102 or any of the other modules inthe spoken dialog system. Further, the modules of system 100 may operateindependent of a full dialog system. For example, a computing devicesuch as a smartphone (or any processing device having an audioprocessing capability, for example a PDA with audio and a WiFi networkinterface) may have an ASR module wherein a user may say “call mom” andthe smartphone may act on the instruction without a “spoken dialoginteraction”.

FIG. 2 illustrates an exemplary processing system 200 in which one ormore of the modules of system 100 may be implemented. Thus, system 100may include at least one processing system, such as, for example,exemplary processing system 200. System 200 may include a bus 210, aprocessor 220, a memory 230, a read only memory (ROM) 240, a storagedevice 250, an input device 260, an output device 270, and acommunication interface 280. Bus 210 may permit communication among thecomponents of system 200. Processor 220 may include at least oneconventional processor or microprocessor that interprets and executesinstructions. Memory 230 may be a random access memory (RAM) or anothertype of dynamic storage device that stores information and instructionsfor execution by processor 220. Memory 230 may also store temporaryvariables or other intermediate information used during execution ofinstructions by processor 220. ROM 240 may include a conventional ROMdevice or another type of static storage device that stores staticinformation and instructions for processor 220. Storage device 250 mayinclude any type of media, such as, for example, magnetic or opticalrecording media and its corresponding drive. Tangible computer-readablestorage media, computer-readable storage devices, or computer-readablememory devices, expressly exclude media such as transitory waves,energy, carrier signals, electromagnetic waves, and signals per se.

Input device 260 may include one or more conventional mechanisms thatpermit a user to input information to system 200, such as a keyboard, amouse, a pen, a microphone, a voice recognition device, etc. Outputdevice 270 may include one or more conventional mechanisms that outputinformation to the user, including a display, a printer, one or morespeakers, or a medium, such as a memory, or a magnetic or optical diskand a corresponding disk drive. Communication interface 280 may includeany transceiver-like mechanism that enables system 200 to communicatevia a network. For example, communication interface 280 may include amodem, or an Ethernet interface for communicating via a local areanetwork (LAN). Alternatively, communication interface 280 may includeother mechanisms for communicating with other devices and/or systems viawired, wireless or optical connections. In some implementations ofnatural spoken dialog system 100, communication interface 280 may not beincluded in processing system 200 when natural spoken dialog system 100is implemented completely within a single processing system 200.

System 200 may perform functions in response to processor 220 executingsequences of instructions contained in a computer-readable medium, suchas, for example, memory 230, a magnetic disk, or an optical disk. Suchinstructions may be read into memory 230 from another computer-readablemedium, such as storage device 250, or from a separate device viacommunication interface 280.

Reusable Library Components

Data for a new application of a natural language spoken dialog systemare typically collected and transcribed. A user experience (UE) expertmay help to define the new application by evaluating an initial set oftranscribed utterances and determining relevant labels or call-types andnamed entities for these utterances. Some examples of call-types mayinclude for example, customer service request (“I would like to be addedto your mailing list”), or customer service complaint (“I would like toreport a problem with my service”).

The UE expert may also select positive (label applies) and negative(label does not apply) guideline utterances for each label (orcall-type). These guideline utterances and descriptions of the labelsmay be included in an annotation guide. The annotation guide may beorganized by category areas where call-types within the same categorymay be grouped together (for example, “Billing Queries” might be one ofthe categories). A set of labelers may use the annotation guide to labeladditional transcribed utterances.

A library of reusable components may include spoken languageunderstanding (SLU) models, automatic speech recognition (ASR) models,named entity grammars or models, manual transcriptions, ASRtranscriptions, call-type labels, audio data (utterances), dialog leveltemplates, prompts, and other reusable data. [Note: a dialog template isa parameterized portion of the call flow to perform a specific task, forexample, collecting the user's SSN. In other words, it is similar to theconcept of function calls in a traditional software library where thefunction arguments describe the input/output parameters. In the DMtemplate case, and especially for natural language dialogs, in additionto the usual parameters such as prompts and grammars, there areexceptions that have to be handled in the context of the wholeapplication. These are called context shifts. Imagine the system askingfor a confirmation “Do you want your bill summary?” (yes/no question)and the user replying with “No, I'd rather have it faxed to my homenumber”. The DM template has to capture and handle this context shiftwhich is domain dependent (yes/no questions are generic) and send itback to the main context shift handler. So, it is typical to usetemplates from a library that are cloned and modified in the context ofthe specific dialog (changes in the specific application context willnot propagate back to the library)]. Thus, the library may include acollection of data from existing natural language spoken dialog systems.

The effort involved in maintaining a library has many benefits. Forexample, defining an extensible taxonomy of call-type categories maypromote uniformity and reduce time and effort required when a new set ofdata is encountered. Moreover, a library may add organization that helpsdocument the natural language spoken dialog system and may be used tobootstrap future natural language spoken dialog systems.

Data Organization of Reusable Components

The data may be organized in various ways. For instance, in animplementation consistent with the principles of the invention, the datamay be organized by industrial sector, such as, for example, financial,healthcare, insurance, etc. Thus, for example, to create a new naturallanguage spoken dialog system in the healthcare sector, all the librarycomponents from the healthcare sector could be used to bootstrap the newnatural language spoken dialog system. Alternatively, in otherimplementations consistent with the principles of the invention the datamay be organized by category (e.g., Service Queries, Billing Queries,etc.) or according to call-types of individual utterances, or by wordsin the utterances such as, for example, frequently occurring words inutterances.

Any given utterance may belong to one or more call-types. Call-types maybe given mnemonic names and textual descriptions to help describe theirsemantic scope. In some implementations, call-types can be assignedattributes which may be used to assist in library management, browsing,and to provide a level of discipline to the call-type design process.Attributes may indicate whether the call-type is generic, reusable, orspecific to a given application. Call-types may include a categoryattribute or at a lower level may be characterized by a “verb” attributesuch as “Request, Report, Ask, etc.” A given call-type may belong to asingle industrial sector or to multiple industrial sectors. The UEexpert may make a judgment call with respect to how to organize variousapplication data sets into industrial sectors. Because the collection ofutterances for any particular application is usually done in phases,each new application may have data sets from several data collectionperiods. Thus, each call-type may also have an attribute describing thedata collection data set.

FIG. 3 illustrates an exemplary architecture of library 300 consistentwith the principles of the invention. Library 300 may include a group ofdatasets 302-1, 302-2, 302-3, . . . ,302-N (collectively referred to as302) on a computer-readable medium. In one implementation, each of thedatasets may include data for a particular industrial sector. Forexample, sector 302-1 may have data pertaining to a financial sector,sector 302-2 may have data pertaining to a healthcare sector, sector302-3 may have data pertaining to an insurance sector, and sector 302-Nmay have data pertaining to another sector.

Each of sectors 302 may include an SLU model, an ASR model, and namedentity grammars or models and may have the same data organization. Anexemplary data organization of a sector, such as financial sector 302-1,is illustrated in FIG. 3. As previously mentioned, data may be collectedin a number of phases. The data collected in a phase is referred to as acollection. Financial sector 302-1 may have a number of collections304-1, 304-2, 304-3, . . . , 304-M (collectively referred to as 304).Collections 304 may share one or more call-types 306-1, 306-2, 306-3, .. . , 306-L (collectively referred to as 306). Each of call-types 304may be associated with utterance data 308. Each occurrence of utterancedata 308 may include a category, for example, Billing Queries, or averb, for example, Request or Report. Utterance data 308 may alsoinclude one or more positive utterance items and one or more negativeutterance items. Each positive or negative utterance item may includeaudio data in a form of an audio recording, a manual or ASRtranscription of the audio data, and one or more call-type labelsindicating the one or more call-types 306 to which the utterance datamay be associated.

One of ordinary skill in the art would understand that the audio dataand corresponding transcriptions may be used to train ASR module 102,and the call-type labels may be used to build new spoken languageunderstanding (SLU) models.

The labeled and transcribed data for each of data collections 304 may beimported into separate data collection databases. In one implementationconsistent with the principles of the invention, the data collectiondatabases may be XML databases (data stored in XML), which may keeptrack of the number of utterances imported from each natural languagespeech dialog application as well as data collection dates. XMLdatabases or files may also include information describing locations ofrelevant library components on the computer-readable medium includinglibrary 300. In other implementations, other types of databases may beused instead of XML databases. For example, in one implementationconsistent with the principles of the invention a relational database,such as, for example, a SQL database may be used.

The data for each collection may be maintained in a separate filestructure. As an example, for browsing application data, it may beconvenient to represent the hierarchical structure as a tree {category,verb, call-type, utterance items}. A call-type library hierarchy may begenerated from the individual data collection databases and the sectordatabase. The call-type library hierarchy may be {sector, datacollection, category, verb, call-type, utterance items}. However, usersmay be interested in all of the call-types with “verb=Request” whichsuggest that the library may be maintained in a relational database. Inone implementation that employs XML databases, widely available toolscan be used, such as tools that support, for example, XML or XPath torender interactive user interfaces with standard web browser clients.XPath is a language for addressing parts of an XML document. XSLT is alanguage for transforming XML documents into other XML documents.

In some implementations consistent with the principles of the invention,methods for building SLU models, for example, text normalization,feature extraction, and named entity extraction methods, may be storedin a file, such as an XML file or other type of file, so that themethods used to build the SLU models may be tracked. Similarly, inimplementations consistent with the principles of the invention, datathat is relevant to building an ASR module or dialog manager may besaved.

FIGS. 4 and 5 are flowcharts that help to explain an exemplary processof building a library of reusable components for building a naturallanguage speech dialog system. The process may begin building a firstcollection of a dataset (act 402). Each collection may be a collectionof data collected during a particular time or data collection phase.

FIG. 5 illustrates an exemplary process (act 402) that may be used tobuild a collection of a dataset. First, call-type or label informationfrom a particular phase of data collection for an existing applicationmay be stored in the collection (act 502). Next, utterance datacollected for the application during the particular data collectionphase may be stored in the collection (act 504). Finally, each item ofstored call-type or label information may be associated with one or moreoccurrences of stored utterance data.

Referring back to FIG. 4, a check may be performed to determine whetherany additional data from the existing application exists with respect toanother phase of data collection (act 404). If the additional dataexists, then acts 502-506 may be performed to build another collectionfrom the existing application (act 406). Otherwise, the builtcollections are stored in the dataset (act 408) and a check is performedto determine whether additional datasets are to be built from theexisting application data (act 410). As previously mentioned, eachdataset may contain data for a particular application, a sector such as,for example, an industrial sector, or a category of data. If moredatasets are to be built, then acts 402-410 may be repeated. Otherwise,the library building process is completed.

Those of ordinary skill in the art will appreciate that otherembodiments of the invention may be practiced in network computingenvironments with many types of computer system configurations,including, for example, personal computers, hand-held devices,multi-processor systems, microprocessor-based or programmable consumerelectronics, network PCs, minicomputers, mainframe computers, and thelike. Embodiments may also be practiced in distributed computingenvironments where tasks are performed by local and remote processingdevices that are linked (either by hardwired links, wireless links, orby a combination thereof) through a communications network. In adistributed computing environment, program modules may be located inboth local and remote memory storage devices. A tangiblecomputer-readable medium is an example of a memory storage device. Thetangible computer-readable medium excludes software per se, energy orwireless interface. Such tangible computer-readable medium includeshardware memory components such as RAM 230, ROM 240, a hard drive 250 orthe like. Thus, any such connection is properly termed acomputer-readable medium.

Although the above description may contain specific details, they shouldnot be construed as limiting the claims in any way. Other configurationsof the described embodiments of the invention are part of the scope ofthis invention. For example, alternative methods of organizing reusablecomponents stored in datasets may be used in implementations consistentwith the principles of the invention. Further, the acts described inFIGS. 4 and 5 may be performed in a different order and still achievedesirable results. Accordingly, other embodiments are within the scopeof the following claims.

We claim as our invention:
 1. A method comprising: collecting aplurality of audible utterances, via a plurality of industry specificspoken dialog systems, and during a plurality of collection phasescomprising respective defined periods of time, to yield stored audibleutterances; organizing, via a processor, the stored audible utterancesinto a plurality of datasets having call-type labels, wherein eachdataset in the plurality of datasets pertains to a unique industrialsector in a plurality of industrial sectors, and wherein the eachdataset for its respective unique industrial sector is independent ofother datasets in the plurality of industrial sectors; generating anannotation guide comprising mnemonic names, textual descriptions, andboth a positive example utterance and a negative example utterance foran associated call-type; and building a natural language spoken dialogsystem using the plurality of datasets and the annotation guide, whereindatasets having a call-type label associated with the negative exampleutterance of the annotation guide are not included in the naturallanguage spoken dialog system.
 2. The method of claim 1, furthercomprising, prior to the building of the natural language spoken dialogsystem, comparing, for each of the call-type labels, each utterance inthe plurality of audible utterances to the negative example utterancefor the associated call-type.
 3. The method of claim 1, wherein theunique industrial sector of each dataset was collected using acorresponding industry specific spoken dialog system of the plurality ofindustry specific spoken dialog systems.
 4. The method of claim 3,wherein the corresponding industry specific spoken dialog system and theunique industrial sector share a common task domain.
 5. The method ofclaim 1, wherein the plurality of datasets are stored in an extensiblemarkup language database.
 6. The method of claim 1, wherein theplurality of datasets are stored in a relational database.
 7. The methodof claim 1, wherein the plurality of audible utterances are eachassociated with utterance-type category.
 8. A system comprising: aprocessor; and a computer-readable storage medium having instructionsstored which, when executed by the processor, result in the processorperforming operations comprising: collecting a plurality of audibleutterances, via a plurality of industry specific spoken dialog systems,and during a plurality of collection phases comprising respectivedefined periods of time, to yield stored audible utterances; organizingthe stored audible utterances into a plurality of datasets havingcall-type labels, wherein each dataset in the plurality of datasetspertains to a unique industrial sector in a plurality of industrialsectors, and wherein the each dataset for its unique industrial sectoris independent of other datasets in the plurality of industrial sectors;generating an annotation guide comprising mnemonic names, textualdescriptions, and both a positive example utterance and negative exampleutterance for an associated call-type; and building a natural languagespoken dialog system using the plurality of datasets and the annotationguide, wherein datasets having a call-type label associated with thenegative example utterance of the annotation guide are not included inthe natural language spoken dialog system.
 9. The system of claim 8,further comprising, prior to the building of the natural language spokendialog system, comparing, for each of the call-type labels, eachutterance in the plurality of audible utterances to the negative exampleutterance for the associated call-type.
 10. The system of claim 8,wherein the unique industrial sector of each dataset was collected usinga corresponding industry specific spoken dialog system of the industryspecific spoken dialog systems.
 11. The system of claim 10, wherein thecorresponding industry specific spoken dialog system and the uniqueindustrial sector share a common task domain.
 12. The system of claim 8,wherein the plurality of datasets are stored in an extensible markuplanguage database.
 13. The system of claim 8, wherein the plurality ofdatasets are stored in a relational database.
 14. The system of claim 8,wherein the plurality of audible utterances are each associated withutterance-type category.
 15. A computer-readable storage device havinginstructions stored which, when executed by a computing device, resultin the computing device performing operations comprising: collecting aplurality of audible utterances, via a plurality of industry specificspoken dialog systems, and during a plurality of collection phasescomprising respective defined periods of time, to yield stored audibleutterances; organizing the stored audible utterances into a plurality ofdatasets having call-type labels, wherein each dataset in the pluralityof datasets pertains to a unique industrial sector in a plurality ofindustrial sectors, and wherein the each dataset for its respectiveunique industrial sector is independent of other datasets in theplurality of industrial sectors; generating an annotation guidecomprising mnemonic names, textual descriptions, and both a positiveexample utterance and negative example utterance for an associatedcall-type; and building a natural language spoken dialog system usingthe plurality of datasets and the annotation guide, wherein datasetshaving a call-type label associated with the negative example utteranceof the annotation guide are not included in the natural language spokendialog system.
 16. The computer-readable storage device of claim 15,further comprising, prior to the building of the natural language spokendialog system, comparing, for each of the call-type labels, eachutterance in the plurality of audible utterances to the negative exampleutterance for the associated call-type.
 17. The computer-readablestorage device of claim 15, wherein the unique industrial sector of eachdataset was collected using a corresponding industry specific spokendialog system of the industry specific spoken dialog systems.
 18. Thecomputer-readable storage device of claim 17, wherein the correspondingindustry specific spoken dialog system and the unique industrial sectorshare a common task domain.
 19. The computer-readable storage device ofclaim 15, wherein the plurality of datasets are stored in an extensiblemarkup language database.
 20. The computer-readable storage device ofclaim 15, wherein the plurality of datasets are stored in a relationaldatabase.